Genetic algorithm-driven surface-enhanced raman spectroscopy substrate optimization

Bilgin, Buse and Yanık, Cenk and Torun, Hulya and Onbasli, Mehmet Cengiz (2021) Genetic algorithm-driven surface-enhanced raman spectroscopy substrate optimization. Nanomaterials, 11 (11). ISSN 2079-4991

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Surface-enhanced Raman spectroscopy (SERS) is a highly sensitive and molecule-specific detection technique that uses surface plasmon resonances to enhance Raman scattering from analytes. In SERS system design, the substrates must have minimal or no background at the incident laser wavelength and large Raman signal enhancement via plasmonic confinement and grating modes over large areas (i.e., squared millimeters). These requirements impose many competing design constraints that make exhaustive parametric computational optimization of SERS substrates pro-hibitively time consuming. Here, we demonstrate a genetic-algorithm (GA)-based optimization method for SERS substrates to achieve strong electric field localization over wide areas for recon-figurable and programmable photonic SERS sensors. We analyzed the GA parameters and tuned them for SERS substrate optimization in detail. We experimentally validated the model results by fabricating the predicted nanostructures using electron beam lithography. The experimental Raman spectrum signal enhancements of the optimized SERS substrates validated the model predictions and enabled the generation of a detailed Raman profile of methylene blue fluorescence dye. The GA and its optimization shown here could pave the way for photonic chips and components with arbitrary design constraints, wavelength bands, and performance targets.
Item Type: Article
Uncontrolled Keywords: Genetic algorithm; Metasurface; Surface-enhanced Raman spectroscopy
Divisions: Sabancı University Nanotechnology Research and Application Center
Depositing User: Cenk Yanık
Date Deposited: 27 Aug 2022 23:17
Last Modified: 27 Aug 2022 23:17

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